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56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
# Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
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"""Class for ControlNet model loading in InvokeAI."""
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from typing import Optional
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from diffusers import ControlNetModel
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from invokeai.backend.model_manager import (
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AnyModel,
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AnyModelConfig,
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)
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from invokeai.backend.model_manager.config import (
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BaseModelType,
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ControlNetCheckpointConfig,
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ModelFormat,
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ModelType,
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SubModelType,
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)
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from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
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from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
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@ModelLoaderRegistry.register(
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base=BaseModelType.StableDiffusion1, type=ModelType.ControlNet, format=ModelFormat.Diffusers
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)
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@ModelLoaderRegistry.register(
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base=BaseModelType.StableDiffusion1, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
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)
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@ModelLoaderRegistry.register(
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base=BaseModelType.StableDiffusion2, type=ModelType.ControlNet, format=ModelFormat.Diffusers
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)
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@ModelLoaderRegistry.register(
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base=BaseModelType.StableDiffusion2, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
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)
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@ModelLoaderRegistry.register(
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base=BaseModelType.StableDiffusionXL, type=ModelType.ControlNet, format=ModelFormat.Diffusers
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)
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@ModelLoaderRegistry.register(
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base=BaseModelType.StableDiffusionXL, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
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)
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class ControlNetLoader(GenericDiffusersLoader):
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"""Class to load ControlNet models."""
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def _load_model(
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self,
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config: AnyModelConfig,
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submodel_type: Optional[SubModelType] = None,
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) -> AnyModel:
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if isinstance(config, ControlNetCheckpointConfig):
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return ControlNetModel.from_single_file(
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config.path,
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torch_dtype=self._torch_dtype,
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)
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else:
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return super()._load_model(config, submodel_type)
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